Geostatistical Analysis of Topsoil Clay and Hydraulic Conductivity Case Study from Al-Jabal Al-Akhdar Region | ||||
| Alexandria Science Exchange Journal | ||||
| Article 10, Volume 34, January-March - Serial Number 1, March 2013, Page 83-92 PDF (617.23 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/asejaiqjsae.2013.2972 | ||||
 View on SCiNiTO | ||||
| Author | ||||
| Ahmed Y. Habel* | ||||
| Soil & Water Department, Faculty of Agriculture – Omar Al Mukhtar University, El- Bieda - Libya | ||||
| Abstract | ||||
| The present study aimed at applying cokriging to predict the values of the primary variable hydraulic conductivity, which is sparsely sampled and hard to measure, using the information content of topsoil Clay content, which is densely sampled and easy to measure, taking into consideration the fact that these two variables are correlated. Regression analysis of both clay percentage and hydraulic conductivity indicated a negatively highly correlated two variables, which satisfies the need to carry out cokriging analysis of hydraulic conductivity using the information content of clay %. The correlation coefficient for this analysis is -0.815. The semi-variogram for clay was fitted to the Gaussian model. While, the semivariogram for hydraulic conductivity was fitted to the Spherical model. The cross-semivariogram of clay and hydraulic conductivity is of the collocated type, which means that the estimation was performed using variables measured at the same location. The most important parameter in this estimation is the high R2 (0.76) obtained from the fitting process. This slightly high estimation regression coefficient comes helps in explain the spatial distribution of both variables. The advantage of cokriging over linear regression is that it takes into consideration the spatial variability of the surrounding points, rather than performing blindly the linear regression, which lacks this improvement. | ||||
| Keywords | ||||
| geostatistics; semivariogram; cokriging analysis; Physical properties; spatial linear regression | ||||
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